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Sorokin EP, Basty N, Whitcher B, Liu Y, Bell JD, Cohen RL, Cule M, Thomas EL. Analysis of MRI-derived spleen iron in the UK Biobank identifies genetic variation linked to iron homeostasis and hemolysis. Am J Hum Genet 2022; 109:1092-1104. [PMID: 35568031 PMCID: PMC9247824 DOI: 10.1016/j.ajhg.2022.04.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
The spleen plays a key role in iron homeostasis. It is the largest filter of the blood and performs iron reuptake from old or damaged erythrocytes. Despite this role, spleen iron concentration has not been measured in a large, population-based cohort. In this study, we quantify spleen iron in 41,764 participants of the UK Biobank by using magnetic resonance imaging and provide a reference range for spleen iron in an unselected population. Through genome-wide association study, we identify associations between spleen iron and regulatory variation at two hereditary spherocytosis genes, ANK1 and SPTA1. Spherocytosis-causing coding mutations in these genes are associated with lower reticulocyte volume and increased reticulocyte percentage, while these common alleles are associated with increased expression of ANK1 and SPTA1 in blood and with larger reticulocyte volume and reduced reticulocyte percentage. As genetic modifiers, these common alleles may explain mild spherocytosis phenotypes that have been observed clinically. Our genetic study also identifies a signal that co-localizes with a splicing quantitative trait locus for MS4A7, and we show this gene is abundantly expressed in the spleen and in macrophages. The combination of deep learning and efficient image processing enables non-invasive measurement of spleen iron and, in turn, characterization of genetic factors related to the lytic phase of the erythrocyte life cycle and iron reuptake in the spleen.
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Affiliation(s)
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, CA, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | | | | | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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Ji Y, Chen R, Wang Q, Wei Q, Tao R, Li B. Leveraging Gene-Level Prediction as Informative Covariate in Hypothesis Weighting Improves Power for Rare Variant Association Studies. Genes (Basel) 2022; 13:genes13020381. [PMID: 35205424 PMCID: PMC8872452 DOI: 10.3390/genes13020381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 01/31/2022] [Accepted: 02/09/2022] [Indexed: 02/05/2023] Open
Abstract
Gene-based rare variant association studies (RVASs) have low power due to the infrequency of rare variants and the large multiple testing burden. To correct for multiple testing, traditional false discovery rate (FDR) procedures which depend solely on P-values are often used. Recently, Independent Hypothesis Weighting (IHW) was developed to improve the detection power while maintaining FDR control by leveraging prior information for each hypothesis. Here, we present a framework to increase power of gene-based RVASs by incorporating prior information using IHW. We first build supervised machine learning models to assign each gene a prediction score that measures its disease risk, using the input of multiple biological features, fed with high-confidence risk genes and local background genes selected near GWAS significant loci as the training set. Then we use the prediction scores as covariates to prioritize RVAS results via IHW. We demonstrate the effectiveness of this framework through applications to RVASs in schizophrenia and autism spectrum disorder. We found sizeable improvements in the number of significant associations compared to traditional FDR approaches, and independent evidence supporting the relevance of the genes identified by our framework but not traditional FDR, demonstrating the potential of our framework to improve power of gene-based RVASs.
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Affiliation(s)
- Ying Ji
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA
| | - Rui Chen
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA
| | - Quan Wang
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA
| | - Qiang Wei
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA
| | - Ran Tao
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
- Correspondence: (R.T.); (B.L.)
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN 37232, USA; (Y.J.); (R.C.); (Q.W.); (Q.W.)
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN 37203, USA
- Correspondence: (R.T.); (B.L.)
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Mullaert J, Bouaziz M, Seeleuthner Y, Bigio B, Casanova JL, Alcaïs A, Abel L, Cobat A. Taking population stratification into account by local permutations in rare-variant association studies on small samples. Genet Epidemiol 2021; 45:821-829. [PMID: 34402542 DOI: 10.1002/gepi.22426] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 06/07/2021] [Accepted: 07/15/2021] [Indexed: 11/08/2022]
Abstract
Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individual phenotypes are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.
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Affiliation(s)
- Jimmy Mullaert
- Université de Paris, IAME, INSERM, Paris, France.,AP-HP, Hôpital Bichat, DEBRC, Paris, France.,Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France
| | - Matthieu Bouaziz
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Yoann Seeleuthner
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Benedetta Bigio
- St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
| | - Jean-Laurent Casanova
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France.,St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA.,Howard Hughes Medical Institute, New York, New York, USA
| | - Alexandre Alcaïs
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
| | - Laurent Abel
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France.,St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, New York, USA
| | - Aurélie Cobat
- Laboratory of Human Genetics of Infectious Diseases, Paris, EU, France.,Université de Paris, Imagine Institute, Paris, EU, France
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Posner DC, Lin H, Meigs JB, Kolaczyk ED, Dupuis J. Convex combination sequence kernel association test for rare-variant studies. Genet Epidemiol 2020; 44:352-367. [PMID: 32100372 DOI: 10.1002/gepi.22287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/17/2019] [Accepted: 01/27/2020] [Indexed: 02/06/2023]
Abstract
We propose a novel variant set test for rare-variant association studies, which leverages multiple single-nucleotide variant (SNV) annotations. Our approach optimizes a convex combination of different sequence kernel association test (SKAT) statistics, where each statistic is constructed from a different annotation and combination weights are optimized through a multiple kernel learning algorithm. The combination test statistic is evaluated empirically through data splitting. In simulations, we find our method preserves type I error at α = 2.5 × 1 0 - 6 and has greater power than SKAT(-O) when SNV weights are not misspecified and sample sizes are large ( N ≥ 5 , 000 ). We utilize our method in the Framingham Heart Study (FHS) to identify SNV sets associated with fasting glucose. While we are unable to detect any genome-wide significant associations between fasting glucose and 4-kb windows of rare variants ( p < 1 0 - 7 ) in 6,419 FHS participants, our method identifies suggestive associations between fasting glucose and rare variants near ROCK2 ( p = 2.1 × 1 0 - 5 ) and within CPLX1 ( p = 5.3 × 1 0 - 5 ). These two genes were previously reported to be involved in obesity-mediated insulin resistance and glucose-induced insulin secretion by pancreatic beta-cells, respectively. These findings will need to be replicated in other cohorts and validated by functional genomic studies.
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Affiliation(s)
- Daniel C Posner
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Honghuang Lin
- National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, Massachusetts.,Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Eric D Kolaczyk
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts.,National Heart Lung and Blood Institute's, Boston University's Framingham Heart Study, Framingham, Massachusetts
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Abstract
Schizophrenia is a highly heritable psychiatric disorder that affects 1% of the population. Genome-wide association studies have identified common variants in candidate genes associated with schizophrenia, but the genetics mechanisms of this disorder have not yet been elucidated. The discovery of rare genetic variants that contribute to schizophrenia symptoms promises to help explain the missing heritability of the disease. Next generation sequencing techniques are revolutionizing the field of psychiatric genetics. Various statistical approaches have been developed for rare variant association testing in case-control and family studies. Targeted resequencing, whole exome sequencing and whole genome sequencing combined with these computational tools are used for the discovery of rare genetic variations in schizophrenia. The findings provide useful information for characterizing the rare mutations and elucidating the genetic mechanisms by which the variants cause schizophrenia.
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Affiliation(s)
- Raina Rhoades
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - Fatimah Jackson
- Department of Biology, Howard University, Washington, DC 20059, USA
| | - Shaolei Teng
- Department of Biology, Howard University, Washington, DC 20059, USA
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